Event-based input and state estimation for linear discrete time-varying systems

Liang Hu, Zidong Wang, Qing-long Han, Xiaohui Liu

Research output: Contribution to journalArticlepeer-review

19 Citations (Scopus)
244 Downloads (Pure)


In this paper, the joint input and state estimation problem is considered for linear discrete-time stochastic systems. An event-based transmission scheme is proposed with which the current measurement is released to the estimator only when the difference from the previously transmitted one is greater than a prescribed threshold. The purpose of this paper is to design an event-based recursive input and state estimator such that the estimation error covariances have guaranteed upper bounds at all times. The estimator gains are calculated by solving two constrained optimisation problems and the upper bounds of the estimation error covariances are obtained in form of the solution to Riccati-like difference equations. Special efforts are made on the choices of appropriate scalar parameter sequences in order to reduce the upper bounds. In the special case of linear time-invariant system, sufficient conditions are acquired under which the upper bound of the error covariance of the state estimation is asymptomatically bounded. Numerical simulations are conducted to illustrate the effectiveness of the proposed estimation algorithm.
Original languageEnglish
Number of pages13
JournalInternational Journal of Control
Early online date01 Feb 2017
Publication statusEarly online date - 01 Feb 2017


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